This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.

Uroflowmetry (UF) is a crucial guideline-recommended tool for men with benign prostatic obstruction (BPO). Moreover, UF is a helpful decision-making tool for the management of patients with lower urinary tract symptoms (LUTS) and benign prostatic hyperplasia (BPH). In the last few years, telemedicine and telehealth have increased exponentially as cost-effective treatment options for both patients and physicians. Telemedicine and telehealth have been well positioned during the COVID-19 pandemic to prevent healthcare system overload and to ensure adequate management of patients through screening, diagnosis, and follow-up at home. In the present manuscript, the main characteristics and performance of a novel and low-cost device for home-based UF have been analyzed. The simple weight-transducer method has been applied to perform UF. An inexpensive load cell connected to a 24 bit analogic digital converter (ADC) sends data to a cloud server via SIM card or home Wi-Fi. Data are processed and shown in graphics with both volume and flow rate as a function of time, allowing for measurement of average flow rate, maximum flow rate, voided volume, and voiding time. A numerical algorithm allows for filtering of the dynamic effect due to the urine gravity acceleration and for removing the funnel to simplify the home measurement procedure. Through an online platform, the physician can see and compare each UF data. The device’s reliability has been validated in a first laboratory setting and showed excellent performance. This approach based on domiciliary tests and an online platform can revolutionize the urologic clinic landscape by offering a constant patient cost-effective follow-up, eliminating the time wasted waiting in the office setting.

Reducing or optimizing electrical power consumption is one of the most fundamental goals within the current frameworks of smart energy management. This approach would be spread from the industrial plants, characterized by high consumptions often well distributed throughout the day, down to the domestic utilities, typically discontinuous and with limited consumption at the single user level. More specifically, it is desirable for the latter case to be able to control in a simple and effective way the power consumption of typical household appliances by means of technologies that are already used and spread (such as tablets and smartphones) to become aware of their actual impact, both economic and environmental. To this aim, the authors present the proof-of-principle of user-friendly monitoring system for power consumption awareness based on the recent technologies of Internet of Things (IoT) and Augmented Reality (AR). In particular, common devices such as smartphones associated along with appropriate measurement nodes and a suitable app, developed to the purpose, allow consumers to view in AR environment electrical consumption of their domestic electrical loads to consciously decide whether to switch them off. Performance of both sensor nodes and AR environment were preliminarily assessed in either laboratory experiments or actual household context, highlighting the promising effectiveness of the proposed approach.